A method for extracting temperature characteristic signals during sulfide ore induced spontaneous combustion

Abstract

The present invention discloses a method for extracting temperature characteristic signals during the induced spontaneous combustion of sulfide ores. The method comprises the following steps: 1. collecting temperature signals during the induced spontaneous combustion of sulfide ores; 2. wavelet decomposition and reconstruction of the temperature increment sequence; 3. obtaining statistical characteristic values of the temperature increment sequence and the low-frequency reconstruction sequence; 4. obtaining standardized indicators of each low-frequency reconstruction sequence relative to the original sequence; 5. obtaining the optimal wavelet function of each measuring point using a fuzzy comprehensive evaluation method; 6. performing high-frequency temperature reconstruction on each measuring point based on the optimal wavelet function of each measuring point to obtain the high-frequency temperature reconstruction sequence of each measuring point. The method of the present invention is simple and reasonably designed. By obtaining the temperature characteristic signal through the optimal wavelet function, the influence of the gradient temperature rise of the test chamber during the induced spontaneous combustion process is eliminated, thereby laying a foundation for subsequent research on the non-steady-state spontaneous combustion characteristics and early warning of spontaneous combustion of sulfide ores.

Description

Technical Field

[0001] The invention belongs to the technical field of sulfide ore spontaneous combustion characteristic signal extraction, and in particular relates to a method for extracting temperature characteristic signals during the sulfide ore induced spontaneous combustion process. Background Art

[0002] Sulfide ore spontaneous combustion fires are a frequent occurrence in high-sulfur mines, causing significant economic losses to mining companies and raising a range of safety and environmental concerns. For example, spontaneous combustion can destroy large quantities of ore, seriously impacting national mineral resource security. The toxic and harmful gases produced by spontaneous combustion pose a serious threat to the health and life of workers, and, once released to the surface, can also pollute the environment. Spontaneous combustion can also trigger explosive explosions, posing a significant threat to mine safety. Therefore, accurately detecting the precursors to spontaneous combustion and providing early warning are crucial.

[0003] Currently, early warning of spontaneous combustion is often achieved through laboratory experiments, field tests, and numerical simulations. For example, a mathematical model of sulfide ore spontaneous combustion has been developed to study the combustion process. However, numerical simulations rely on a simplification of the actual problem. Field tests can measure indicators such as ambient temperature and humidity in the mine, surface and internal temperatures of the ore pile, and SO₂ and O₂ concentrations, providing high confidence. However, field tests are time-consuming and difficult to effectively control. Therefore, to shorten the experimental period and more effectively control the experimental process, small-scale laboratory experiments are currently being used instead of large-scale field tests.

[0004] Small-scale ore piles used in indoor experiments are difficult to generate due to the difficulty in generating a heat-collecting and temperature-raising environment, making spontaneous combustion difficult. Therefore, they must be placed in a test chamber and heated to induce spontaneous combustion. Given that the temperature of the ore pile fluctuates throughout the entire oxidation-induced spontaneous combustion process and is easily measured, indoor experiments often use this temperature change as an indicator for early monitoring of the extent of spontaneous combustion. However, the temperature changes at various measuring points within the ore pile during the induced spontaneous combustion process are the result of a combination of the chamber's temperature gradient and the ore's self-heating from oxidation, with the chamber's temperature gradient playing a dominant role.

[0005] Therefore, a method is needed to extract the spontaneous combustion characteristic signal from the measured temperature sequence of sulfide ore during the induced spontaneous combustion process, eliminate the influence of the gradient temperature rise of the test chamber during the induced spontaneous combustion process, and thus facilitate the study of the non-steady-state spontaneous combustion characteristics of sulfide ores and early warning of spontaneous combustion based on the temperature characteristic signal. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to address the deficiencies in the above-mentioned prior art and provide a method for extracting temperature characteristic signals during the induced spontaneous combustion process of sulfide ores. The method has simple steps and a reasonable design. The temperature characteristic signals are obtained through the optimal wavelet function, eliminating the influence of the gradient temperature rise of the test chamber during the induced spontaneous combustion process, thereby laying the foundation for subsequent research on the non-steady-state spontaneous combustion characteristics and early warning of spontaneous combustion of sulfide ores, and has strong practicality.

[0007] To solve the above technical problems, the technical solution adopted by the present invention is: a method for extracting temperature characteristic signals during sulfide ore induced spontaneous combustion, characterized by: step 1, collecting temperature signals during sulfide ore induced spontaneous combustion:

[0008] Step 101: setting a plurality of measuring points in a sulfide ore pile in a test chamber, and setting a temperature sensor probe at each measuring point; wherein the number of the measuring points and the temperature sensor probes is N;

[0009] Step 102: The i-th temperature sensor collects the temperature of the i-th measuring point according to the preset sampling interval, obtains the measured temperature sequence of the i-th measuring point and records it as T i (1), ..., T i (m), ..., T i (M), T i (M+1); where m and M are both positive integers, and 1≤m≤M+1, M+1 is the length of the sequence, m represents the sampling number, i is a positive integer, and 1≤i≤N;

[0010] Step 103: When m is 1 to M, according to T' i (m) = T i (m+1)-T i (m), and obtain the temperature increment T′ of the m+1th sampling point relative to the mth sampling point of the i-th measuring point i (m);

[0011] Step 104: Repeat step 103 multiple times to obtain the temperature increment sequence of the i-th measuring point and record it as T′ i (1), ..., T′ i (m), ..., T′ i (M); where the length of the temperature increment sequence is M;

[0012] Step 2: Wavelet decomposition and reconstruction of temperature increment sequence:

[0013] Step 201: Decompose the temperature increment sequence of the i-th measuring point by one layer using the first wavelet function using a computer to obtain low-frequency coefficients after wavelet decomposition;

[0014] Step 202: Reconstruct the low-frequency coefficients using a computer to obtain a first low-frequency reconstruction sequence;

[0015] Step 203, repeating steps 201 to 202 multiple times, using the Eth wavelet function to perform decomposition and reconstruction, to obtain the Eth low-frequency reconstruction sequence; wherein E is a positive integer;

[0016] Step 3: Obtain statistical characteristic values of the temperature increment sequence and the low-frequency reconstruction sequence: wherein the statistical characteristic values include mean, coefficient of variation, first-order autocorrelation coefficient, and skewness coefficient;

[0017] Step 4: Obtaining the standardized index of each low-frequency reconstructed sequence relative to the original sequence;

[0018] Step 5: Use fuzzy comprehensive evaluation method to obtain the optimal wavelet function of each measuring point;

[0019] Step 6: reconstruct the temperature of each measuring point at a high frequency according to the optimal wavelet function of each measuring point to obtain the temperature high frequency reconstruction sequence of each measuring point; wherein the temperature high frequency reconstruction sequence of each measuring point serves as the temperature characteristic signal of each measuring point.

[0020] The above-mentioned method for extracting temperature characteristic signals during sulfide ore induced spontaneous combustion is characterized in that: in step 3, the statistical characteristic values of the temperature increment sequence and the low-frequency reconstruction sequence are obtained, and the specific process is as follows:

[0021] Step 301: Obtain the statistical characteristic value of the temperature increment sequence of the i-th measuring point; wherein the statistical characteristic value of the temperature increment sequence of the i-th measuring point includes the mean of the original sequence The coefficient of variation C of the original sequence V0 , the first-order autocorrelation coefficient r of the original sequence 10 and the skewness coefficient C of the original sequence s0 ;

[0022] Step 302: Obtain the statistical characteristic value of the e-th low-frequency reconstructed sequence; wherein the statistical characteristic value of the e-th low-frequency reconstructed sequence includes the e-th mean The e-th coefficient of variation The e-th first-order autocorrelation coefficient and the e-th skewness coefficient Wherein, e is a positive integer, and 1≤e≤E.

[0023] The above-mentioned method for extracting temperature characteristic signals during sulfide ore induced spontaneous combustion is characterized in that the specific process of obtaining the index of each low-frequency reconstructed sequence relative to the original sequence in step 4 is as follows:

[0024] Step 401: According to the formula Get the first initial index of the e-th low-frequency reconstructed sequence relative to the original sequence The second initial index of the e-th low-frequency reconstructed sequence relative to the original sequence The third initial index of the e-th low-frequency reconstructed sequence relative to the original sequence The fourth initial index of the e-th low-frequency reconstructed sequence relative to the original sequence Among them, c1 and c4 are constants;

[0025] Step 402: The first initial index of the e-th low-frequency reconstructed sequence relative to the original sequence The second initial index of the e-th low-frequency reconstructed sequence relative to the original sequence The third initial index of the e-th low-frequency reconstructed sequence relative to the original sequence The fourth initial index of the e-th low-frequency reconstructed sequence relative to the original sequence are recorded as the kth initial index of the eth low-frequency reconstructed sequence relative to the original sequence Wherein, k is a positive integer, and the value of k is 1, 2, 3, 4;

[0026] Step 403: Sort the E low-frequency reconstructed sequences relative to the kth initial index of the original sequence from small to large, and obtain the maximum value u of the kth initial index. k0 (max) and the minimum value u of the kth initial index k0 (min);

[0027] Step 404: According to the formula Get the kth normalized index of the eth low-frequency reconstructed sequence relative to the original sequence

[0028] The above-mentioned method for extracting temperature characteristic signals during sulfide ore induced spontaneous combustion is characterized in that the constant c1 is obtained in step 401, and the specific process is as follows:

[0029] Step 4011: E means Sort from small to large and get the maximum mean and minimum mean

[0030] Step 4012: Get the constant c1; where, Indicates rounding up operation;

[0031] The specific process of obtaining the constant c4 in step 401 is as follows:

[0032] Step 401A: E skewness coefficients Sort from small to large and get the maximum skewness coefficient C smax and the minimum skewness coefficient C smin ;

[0033] Step 401B: Get the constant c4; where, Indicates a round-up operation.

[0034] The above-mentioned method for extracting temperature characteristic signals during the sulfide ore induced spontaneous combustion process is characterized in that the method of using the fuzzy comprehensive evaluation method to obtain the optimal wavelet function of each measuring point in step 5 is the same, wherein the fuzzy comprehensive evaluation method is used to obtain the optimal wavelet function of the i-th measuring point, and the specific process is as follows:

[0035] Step 501: Calculate the four standardized indices of the E low-frequency reconstructed sequences of the i-th measurement point relative to the original sequence using the entropy weight method to obtain the weights corresponding to the four indices; the weight corresponding to the k-th indicator is recorded as W k , then the indicator weight matrix is recorded as A = [W1, W2, W3, W4];

[0036] Step 502: Using the fuzzy comprehensive evaluation method, the four indicators of mean, coefficient of variation, first-order autocorrelation coefficient and skewness coefficient are used as the factor set, and the first wavelet function to the Eth wavelet function are used as the evaluation set to determine the evaluation matrix Among them, r ke represents the membership of the kth index relative to the eth wavelet function; and

[0037] Step 503: According to B f =A* f R, get the fth fuzzy comprehensive judgment matrix B f ; Among them, the fuzzy comprehensive judgment matrix B f Record represents the evaluation value of the e-th wavelet function of the f-th fuzzy judgment; where f is a positive integer, and the values of f are 1, 2, 3 and 4;* f represents the fth fuzzy operator;

[0038] Step 504: Get the comprehensive evaluation value S of the e-th wavelet function e ; Where a is a constant and its value is 0.25;

[0039] Step 505: Repeat steps 503 and 504 multiple times to obtain the comprehensive evaluation value S of the Eth wavelet function. E ;

[0040] Step 506: The comprehensive evaluation value S1 of the first wavelet function to the comprehensive evaluation value S E Sorting from small to large, the wavelet function corresponding to the maximum comprehensive evaluation value is the optimal wavelet function of the i-th measuring point.

[0041] The above-mentioned method for extracting temperature characteristic signals of the sulfide ore induced spontaneous combustion process is characterized in that: the first to fourth fuzzy operators in step 503 are M(∧,∨) operators respectively, operator, M(·,∨) operator, Operator.

[0042] The above-mentioned method for extracting temperature characteristic signals during the sulfide ore induced spontaneous combustion process is characterized in that: in step 5, the temperature of each measuring point is reconstructed at a high frequency according to the optimal wavelet function of each measuring point, and the method for obtaining the temperature high-frequency reconstruction sequence of each measuring point is the same. Specifically, the temperature of the i-th measuring point is reconstructed at a high frequency according to the optimal wavelet function of the i-th measuring point to obtain the temperature high-frequency reconstruction sequence of the i-th measuring point. The specific process is as follows:

[0043] A computer is used to decompose the temperature increment sequence of the i-th measuring point using the optimal wavelet function of the i-th measuring point to obtain the high-frequency coefficients after wavelet decomposition; then the computer is reconstructed according to the high-frequency coefficients to obtain the high-frequency reconstructed temperature sequence of the i-th measuring point.

[0044] Compared with the prior art, the present invention has the following advantages:

[0045] 1. The present invention introduces wavelet decomposition and reconstruction technology to decompose the measured temperature increment sequence during the induced spontaneous combustion of sulfide ore into low-frequency components and high-frequency components. The former reflects the influence of heating in the test chamber, while the latter reflects the self-heating effect of the ore's own oxidation, thereby achieving the purpose of extracting the spontaneous combustion characteristic signal from the measured ore temperature sequence.

[0046] 2. The present invention takes into account that different wavelet functions may produce differences in analysis results, and introduces a fuzzy comprehensive evaluation method to quantitatively compare the advantages and disadvantages of different wavelet functions to achieve the purpose of selecting the best wavelet function.

[0047] 3. The low-frequency reconstructed sequence in the present invention shows basically the same trend as the original temperature increment sequence, which verifies that the gradient heating of the test chamber plays a dominant role. The high-frequency reconstructed sequence, on the other hand, contains rich details, reflecting the subtle changes in the entire process of oxidation, self-heating, and spontaneous combustion of the sulfide ore pile. Therefore, the high-frequency reconstructed sequence of the temperature at each measuring point is used as the temperature characteristic signal of each measuring point.

[0048] In summary, the method of the present invention has simple steps and reasonable design. It obtains the optimal wavelet function based on the fuzzy comprehensive evaluation method, obtains the temperature characteristic signal through the optimal wavelet function, eliminates the influence of the gradient temperature rise of the test chamber during the induced spontaneous combustion process, and thus lays the foundation for subsequent research on the non-steady-state spontaneous combustion characteristics and early warning of spontaneous combustion of sulfide ores, and has strong practicality.

[0049] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION

[0051] like Figure 1 As shown, a method for extracting temperature characteristic signals during sulfide ore induced spontaneous combustion process includes the following steps:

[0052] Step 1: Acquisition of temperature signals during sulfide ore induced spontaneous combustion:

[0053] Step 101: setting a plurality of measuring points in a sulfide ore pile in a test chamber, and setting a temperature sensor probe at each measuring point; wherein the number of the measuring points and the temperature sensor probes is N;

[0054] Step 102: The i-th temperature sensor collects the temperature of the i-th measuring point according to the preset sampling interval, obtains the measured temperature sequence of the i-th measuring point and records it as T i (1), ..., T i (m), ..., T i (M), T i (M+1); where m and M are both positive integers, and 1≤m≤M+1, M+1 is the length of the sequence, m represents the sampling number, i is a positive integer, and 1≤i≤N; T i (m) represents the mth temperature value of the i-th measuring point;

[0055] Step 103: When m is 1 to M, according to T' i (m) = T i (m+1)-T i (m), and obtain the temperature increment T′ of the m+1th sampling point relative to the mth sampling point of the i-th measuring point i (m);

[0056] Step 104: Repeat step 103 multiple times to obtain the temperature increment sequence of the i-th measuring point and record it as T′ i (1), ..., T′ i (m), ..., T′ i (M); where the length of the temperature increment sequence is M;

[0057] Step 2: Wavelet decomposition and reconstruction of temperature increment sequence:

[0058] Step 201: Decompose the temperature increment sequence of the i-th measuring point by one layer using the first wavelet function using a computer to obtain low-frequency coefficients after wavelet decomposition;

[0059] Step 202: Reconstruct the low-frequency coefficients using a computer to obtain a first low-frequency reconstruction sequence;

[0060] Step 203, repeating steps 201 to 202 multiple times, using the Eth wavelet function to perform decomposition and reconstruction, to obtain the Eth low-frequency reconstruction sequence; wherein E is a positive integer;

[0061] Step 3: Obtain statistical characteristic values of the temperature increment sequence and the low-frequency reconstruction sequence: wherein the statistical characteristic values include mean, coefficient of variation, first-order autocorrelation coefficient, and skewness coefficient;

[0062] Step 4: Obtaining the standardized index of each low-frequency reconstructed sequence relative to the original sequence;

[0063] Step 5: Use fuzzy comprehensive evaluation method to obtain the optimal wavelet function of each measuring point;

[0064] Step 6: reconstruct the temperature of each measuring point at a high frequency according to the optimal wavelet function of each measuring point to obtain the temperature high frequency reconstruction sequence of each measuring point; wherein the temperature high frequency reconstruction sequence of each measuring point serves as the temperature characteristic signal of each measuring point.

[0065] In this embodiment, the statistical characteristic values of the temperature increment sequence and the low-frequency reconstruction sequence are obtained in step 3. The specific process is as follows:

[0066] Step 301: Obtain the statistical characteristic value of the temperature increment sequence of the i-th measuring point; wherein the statistical characteristic value of the temperature increment sequence of the i-th measuring point includes the mean of the original sequence The coefficient of variation C of the original sequence V0 , the first-order autocorrelation coefficient r of the original sequence 10 and the skewness coefficient C of the original sequence s0 ;

[0067] Step 302: Obtain the statistical characteristic value of the e-th low-frequency reconstructed sequence; wherein the statistical characteristic value of the e-th low-frequency reconstructed sequence includes the e-th mean The e-th coefficient of variation The e-th first-order autocorrelation coefficient and the e-th skewness coefficient Wherein, e is a positive integer, and 1≤e≤E.

[0068] In this embodiment, the specific process of obtaining the index of each low-frequency reconstructed sequence relative to the original sequence in step 4 is as follows:

[0069] Step 401: According to the formula Get the first initial index of the e-th low-frequency reconstructed sequence relative to the original sequence The second initial index of the e-th low-frequency reconstructed sequence relative to the original sequence The third initial index of the e-th low-frequency reconstructed sequence relative to the original sequence The fourth initial index of the e-th low-frequency reconstructed sequence relative to the original sequence Where c1 and c4 are constants; |·| represents the absolute value;

[0070] Step 402: The first initial index of the e-th low-frequency reconstructed sequence relative to the original sequence The second initial index of the e-th low-frequency reconstructed sequence relative to the original sequence The third initial index of the e-th low-frequency reconstructed sequence relative to the original sequence The fourth initial index of the e-th low-frequency reconstructed sequence relative to the original sequence are recorded as the kth initial index of the eth low-frequency reconstructed sequence relative to the original sequence Wherein, k is a positive integer, and the value of k is 1, 2, 3, 4;

[0071] Step 403: Sort the E low-frequency reconstructed sequences relative to the kth initial index of the original sequence from small to large, and obtain the maximum value u of the kth initial index. k0 (max) and the minimum value u of the kth initial index k0 (min);

[0072] Step 404: According to the formula Get the kth normalized index of the eth low-frequency reconstructed sequence relative to the original sequence

[0073] In this embodiment, the specific process of obtaining the constant c1 in step 401 is as follows:

[0074] Step 4011: E means Sort from small to large and get the maximum mean and minimum mean

[0075] Step 4012: Get the constant c1; where, Indicates rounding up operation;

[0076] The specific process of obtaining the constant c4 in step 401 is as follows:

[0077] Step 401A: E skewness coefficients Sort from small to large and get the maximum skewness coefficient C smax and the minimum skewness coefficient C smin ;

[0078] Step 401B: Get the constant c4; where, Indicates a round-up operation.

[0079] In this embodiment, the method of using the fuzzy comprehensive evaluation method to obtain the optimal wavelet function of each measuring point in step 5 is the same. Among them, the fuzzy comprehensive evaluation method is used to obtain the optimal wavelet function of the i-th measuring point. The specific process is as follows:

[0080] Step 501: Calculate the four standardized indices of the E low-frequency reconstructed sequences of the i-th measurement point relative to the original sequence using the entropy weight method to obtain the weights corresponding to the four indices; the weight corresponding to the k-th indicator is recorded as W k , then the indicator weight matrix is recorded as A = [W1, W2, W3, W4];

[0081] Step 502: Using the fuzzy comprehensive evaluation method, the four indicators of mean, coefficient of variation, first-order autocorrelation coefficient and skewness coefficient are used as the factor set, and the first wavelet function to the Eth wavelet function are used as the evaluation set to determine the evaluation matrix Among them, r ke represents the membership of the kth index relative to the eth wavelet function; and

[0082] Step 503: According to B f =A* f R, get the fth fuzzy comprehensive judgment matrix B f ; Among them, the fuzzy comprehensive judgment matrix B f Record represents the evaluation value of the e-th wavelet function of the f-th fuzzy judgment; where f is a positive integer, and the values of f are 1, 2, 3 and 4;* f represents the fth fuzzy operator;

[0083] Step 504: Get the comprehensive evaluation value S of the e-th wavelet function e ; Where a is a constant and its value is 0.25;

[0084] Step 505: Repeat steps 503 and 504 multiple times to obtain the comprehensive evaluation value S of the Eth wavelet function. E ;

[0085] Step 506: The comprehensive evaluation value S1 of the first wavelet function to the comprehensive evaluation value S E Sorting from small to large, the wavelet function corresponding to the maximum comprehensive evaluation value is the optimal wavelet function of the i-th measuring point.

[0086] In this embodiment, the first to fourth fuzzy operators in step 503 are M(∧,∨) operators respectively. operator, M(·,∨) operator, Operator.

[0087] In this embodiment, in step 5, the method for performing high-frequency temperature reconstruction on each measuring point according to the optimal wavelet function of each measuring point and obtaining the high-frequency temperature reconstruction sequence of each measuring point is the same. Specifically, the temperature high-frequency reconstruction is performed on the i-th measuring point according to the optimal wavelet function of the i-th measuring point to obtain the high-frequency temperature reconstruction sequence of the i-th measuring point. The specific process is as follows:

[0088] A computer is used to decompose the temperature increment sequence of the i-th measuring point using the optimal wavelet function of the i-th measuring point to obtain the high-frequency coefficients after wavelet decomposition; then the computer is reconstructed according to the high-frequency coefficients to obtain the high-frequency reconstructed temperature sequence of the i-th measuring point.

[0089] In this example, it should be noted that sulfide ore oxidation and spontaneous combustion is a typical rheological-catastrophic process. Traditionally, this process is determined by experimentally observing changes in the sulfide ore's temperature. However, the temperature changes measured during the induced spontaneous combustion experiment are the result of a combination of the test chamber's temperature gradient and self-heating. Directly performing mutation detection on the measured temperature data can lead to misjudgments. Therefore, a high-frequency reconstructed sequence of temperatures at each measurement point is used as the temperature signature signal for each measurement point.

[0090] In this embodiment, it should be noted that the gradient heating of the test chamber plays a dominant role in the temperature change of the sulfide ore pile, so the mean of the original sequence and the low-frequency sequence are not much different; the low-frequency reconstructed sequence eliminates the complex information of the entire process of oxidation, self-heating, and spontaneous combustion of the ore pile, and therefore, the coefficient of variation should be reduced compared to the original sequence; the low-frequency reconstructed sequence eliminates the complex information of the entire process of oxidation, self-heating, and spontaneous combustion of the ore pile, and therefore the first-order autocorrelation coefficient with the original sequence should be increased; the low-frequency reconstructed sequence occupies a major part of the original sequence, and therefore the original sequence and the low-frequency reconstructed sequence should maintain basically the same skewness and kurtosis, that is, the skewness coefficient is not much different.

[0091] In this embodiment, it should be noted that the length of the sequence is set to M, the value of m is between 1 and M, and the mth value of the sequence is recorded as T m , the m+1th value of the sequence is recorded as T m+1 ; The mean value of the sequence is recorded as μ, and the standard deviation of the sequence is recorded as σ, then the skewness coefficient of the sequence is Coefficient of variation of the sequence

[0092] The first-order autocorrelation coefficient of the series in,

[0093] In this embodiment, the preset sampling interval is 30 seconds.

[0094] In this embodiment, the value of E in step 203 is 55, and the first to 55th wavelet functions are haar, db1, db2, db3, db4, db5, db6, db7, db8, db9, db10, sym1, sym2, sym3, sym4, sym5, sym6, sym7, sym8, coif1, coif2, coif3, coif4, coif5, bior1.1, bior1.3, bior1.5, bior2.2, bior2.4, bior 2.6, bior2.8, bior3.1, bior3.3, bior3.5, bior3.7, bior3.9, bior4.4, bior5.5, bior6.8, dmey, rbio1.1, rbio1.3, rbi o1.5, rbio2.2, rbio2.4, rbio2.6, rbio2.8, rbio3.1, rbio3.3, rbio3.5, rbio3.7, rbio3.9, rbio4.4, rbio5.5, rbio6.8.

[0095] In this embodiment, the value of N is 8, and the optimal wavelet functions of the 8 measuring points are sym2, rbio5.5, bior5.5, bior5.5, db2, db2, db2, and db2, respectively.

[0096] In this embodiment, the length of the temperature increment sequence is not less than 500.

[0097] In this embodiment, is the first mean, is the Eth mean, is the first skewness coefficient, is the Eth skewness coefficient.

[0098] In this embodiment, in the fuzzy operator, ∧ means taking a smaller value, ∨ means taking a larger value, and · means multiplication. Indicates summation.

[0099] In summary, the method of the present invention has simple steps and reasonable design. It obtains the temperature characteristic signal through the optimal wavelet function, eliminates the influence of the gradient temperature rise of the test chamber during the induced spontaneous combustion process, and thus lays the foundation for subsequent research on the non-steady-state spontaneous combustion characteristics and early warning of spontaneous combustion of sulfide ores, and has strong practicality.

[0100] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for extracting temperature characteristic signals during sulfide ore induced spontaneous combustion, characterized in that: The method comprises the following steps: Step 1: Acquisition of temperature signals during sulfide ore induced spontaneous combustion: Step 101: setting a plurality of measuring points in a sulfide ore pile in a test chamber, and setting a temperature sensor probe at each measuring point; wherein the number of the measuring points and the temperature sensor probes is N; Step 102: The i-th temperature sensor collects the temperature of the i-th measuring point according to the preset sampling interval, obtains the measured temperature sequence of the i-th measuring point and records it as T i (1), ..., T i (m), ..., T i (M), T i (M+1); where m and M are both positive integers, and 1≤m≤M+1, M+1 is the length of the sequence, m represents the sampling number, i is a positive integer, and 1≤i≤N; Step 103: When m is 1 to M, according to T' i (m) = T i (m+1)-T i (m), and obtain the temperature increment T′ of the m+1th sampling point relative to the mth sampling point of the i-th measuring point i (m); Step 104: Repeat step 103 multiple times to obtain the temperature increment sequence of the i-th measuring point and record it as T′ i (1), ..., T′ i (m), ..., T′ i (M); where the length of the temperature increment sequence is M; Step 2: Wavelet decomposition and reconstruction of temperature increment sequence: Step 201: Decompose the temperature increment sequence of the i-th measuring point by one layer using the first wavelet function using a computer to obtain low-frequency coefficients after wavelet decomposition; Step 202: Reconstruct the low-frequency coefficients using a computer to obtain a first low-frequency reconstruction sequence; Step 203, repeating steps 201 to 202 multiple times, using the Eth wavelet function to perform decomposition and reconstruction, to obtain the Eth low-frequency reconstruction sequence; wherein E is a positive integer; Step 3: Obtain statistical characteristic values of the temperature increment sequence and the low-frequency reconstruction sequence: wherein the statistical characteristic values include mean, coefficient of variation, first-order autocorrelation coefficient, and skewness coefficient; Step 4: Obtaining the standardized index of each low-frequency reconstructed sequence relative to the original sequence; Step 5: Use fuzzy comprehensive evaluation method to obtain the optimal wavelet function of each measuring point; Step 6: reconstruct the temperature of each measuring point at a high frequency according to the optimal wavelet function of each measuring point to obtain the temperature high frequency reconstruction sequence of each measuring point; wherein the temperature high frequency reconstruction sequence of each measuring point serves as the temperature characteristic signal of each measuring point.

2. The method for extracting temperature characteristic signals during sulfide ore induced spontaneous combustion according to claim 1, characterized in that: In step 3, the statistical characteristic values of the temperature increment sequence and the low-frequency reconstruction sequence are obtained. The specific process is as follows: Step 301: Obtain the statistical characteristic value of the temperature increment sequence of the i-th measuring point; wherein the statistical characteristic value of the temperature increment sequence of the i-th measuring point includes the mean of the original sequence The coefficient of variation C of the original sequence V0 , the first-order autocorrelation coefficient r of the original sequence 10 and the skewness coefficient C of the original sequence s0 ; Step 302: Obtain the statistical characteristic value of the e-th low-frequency reconstructed sequence; wherein the statistical characteristic value of the e-th low-frequency reconstructed sequence includes the e-th mean The e-th coefficient of variation The e-th first-order autocorrelation coefficient and the e-th skewness coefficient Wherein, e is a positive integer, and 1≤e≤E.

3. The method for extracting temperature characteristic signals during sulfide ore induced spontaneous combustion according to claim 2, characterized in that: The specific process of obtaining the index of each low-frequency reconstructed sequence relative to the original sequence in step 4 is as follows: Step 401: According to the formula Get the first initial index of the e-th low-frequency reconstructed sequence relative to the original sequence The second initial index of the e-th low-frequency reconstructed sequence relative to the original sequence The third initial index of the e-th low-frequency reconstructed sequence relative to the original sequence The fourth initial index of the e-th low-frequency reconstructed sequence relative to the original sequence Among them, c1 and c4 are constants; Step 402: The first initial index of the e-th low-frequency reconstructed sequence relative to the original sequence The second initial index of the e-th low-frequency reconstructed sequence relative to the original sequence The third initial index of the e-th low-frequency reconstructed sequence relative to the original sequence The fourth initial index of the e-th low-frequency reconstructed sequence relative to the original sequence are recorded as the kth initial index of the eth low-frequency reconstructed sequence relative to the original sequence Wherein, k is a positive integer, and the value of k is 1, 2, 3, 4; Step 403: Sort the E low-frequency reconstructed sequences relative to the kth initial index of the original sequence from small to large, and obtain the maximum value u of the kth initial index. k0 (max) and the minimum value u of the kth initial index k0 (min); Step 404: According to the formula Get the kth normalized index of the eth low-frequency reconstructed sequence relative to the original sequence 4. The method for extracting temperature characteristic signals during sulfide ore induced spontaneous combustion according to claim 3, characterized in that: The specific process of obtaining the constant c1 in step 401 is as follows: Step 4011: E means Sort from small to large and get the maximum mean and minimum mean Step 4012: Get the constant c1; where, Indicates rounding up operation; The specific process of obtaining the constant c4 in step 401 is as follows: Step 401A: E skewness coefficients Sort from small to large and get the maximum skewness coefficient C smax and the minimum skewness coefficient C smin ; Step 401B: Get the constant c4; where, Indicates a round-up operation.

5. The method for extracting temperature characteristic signals during sulfide ore induced spontaneous combustion according to claim 3, characterized in that: The method of using the fuzzy comprehensive evaluation method to obtain the optimal wavelet function of each measuring point in step 5 is the same. Among them, the fuzzy comprehensive evaluation method is used to obtain the optimal wavelet function of the i-th measuring point. The specific process is as follows: Step 501: Calculate the four standardized indices of the E low-frequency reconstructed sequences of the i-th measurement point relative to the original sequence using the entropy weight method to obtain the weights corresponding to the four indices; the weight corresponding to the k-th indicator is recorded as W k , then the indicator weight matrix is recorded as A = [W1, W2, W3, W4]; Step 502: Using the fuzzy comprehensive evaluation method, the four indicators of mean, coefficient of variation, first-order autocorrelation coefficient and skewness coefficient are used as the factor set, and the first wavelet function to the Eth wavelet function are used as the evaluation set to determine the evaluation matrix Among them, r ke represents the membership of the kth index relative to the eth wavelet function; and Step 503: According to B f =A* f R, get the fth fuzzy comprehensive judgment matrix B f ; Among them, the fuzzy comprehensive judgment matrix B f Record represents the evaluation value of the e-th wavelet function of the f-th fuzzy judgment; where f is a positive integer, and the values of f are 1, 2, 3 and 4;* f represents the fth fuzzy operator; Step 504: Get the comprehensive evaluation value S of the e-th wavelet function e ; Where a is a constant and its value is 0.25; Step 505: Repeat steps 503 and 504 multiple times to obtain the comprehensive evaluation value S of the Eth wavelet function. E ; Step 506: The comprehensive evaluation value S1 of the first wavelet function to the comprehensive evaluation value S E Sorting from small to large, the wavelet function corresponding to the maximum comprehensive evaluation value is the optimal wavelet function of the i-th measuring point.

6. The method for extracting temperature characteristic signals during sulfide ore induced spontaneous combustion according to claim 5, characterized in that: In step 503, the first to fourth fuzzy operators are M(∧,∨) operators respectively. operator, M(·,∨) operator, Operator.

7. The method for extracting temperature characteristic signals during sulfide ore induced spontaneous combustion according to claim 1, characterized in that: In step 5, the temperature of each measuring point is reconstructed at a high frequency according to the optimal wavelet function of each measuring point. The method for obtaining the temperature high-frequency reconstruction sequence of each measuring point is the same. Among them, the temperature of the i-th measuring point is reconstructed at a high frequency according to the optimal wavelet function of the i-th measuring point to obtain the temperature high-frequency reconstruction sequence of the i-th measuring point. The specific process is as follows: A computer is used to decompose the temperature increment sequence of the i-th measuring point using the optimal wavelet function of the i-th measuring point to obtain the high-frequency coefficients after wavelet decomposition; then the computer is reconstructed according to the high-frequency coefficients to obtain the high-frequency reconstructed temperature sequence of the i-th measuring point.

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